Two of the critical obstacles to wider real-world use of plans are the inability of planners to deal with incomplete information, and the slowness of planners in general and in particular, those that do deal with incomplete knowledge.
While most planners assume all information needed to generate the plan is available beforehand, this is not typically the case in realistic planning situations. The system does not know beforehand which gate a flight is going to depart from, for instance. Conditional planning constitutes a solution in those cases in which the facts needed to make a decision will become known during execution, but is not known during planning. A conditional planner generates a conditional plan that, for each alternative for the unknown facts, provides a sequence of actions. The appropriate actions are then chosen during execution based on information gathered. The conditional plan generated is a tree or a graph structure. The problems with this approach are that (1) it is computationally expensive to build these conditional plans (2) the plans can be large and therefore expensive to store and transfer (3) it is expensive to execute these large plans (4) this is not a good approach for all cases of unknown information.
One prior art planner uses a non-traditional planning algorithm in which the state of the world is completely known before the next step is computed. The prior art planners are inefficient and cannot be practically used to solve problems of the size that would be useful in the real world.